The Role of Landscape Context, Management Intensity, and Vegetation Heterogeneity in Shaping Urban Park Multifunctionality – A Case Study of Chengdu
都市公園の多機能性を形成する景観コンテキスト、管理強度、植生不均一性の役割 – 成都を事例として (AI 翻訳)
Kamran Ahmed, Muhammad Bilal, Mudassir Aziz, Saman Asif, Sohail Abbas
🤖 gxceed AI 要約
日本語
本研究は、成都市の60公園を対象に、景観コンテキスト、管理強度、植生不均一性が公園の多機能性(冷却効果、炭素貯留、生物多様性、レクリエーション)に与える影響を分析した。空間回帰の結果、植生不均一性が最も強い正の予測因子であり、管理強度は負の相関を示した。内部構成が公園規模よりも重要であり、空間的に分化した管理が有効である。
English
This study analyzes how landscape context, management intensity, and vegetation heterogeneity shape urban park multifunctionality (cooling, carbon storage, biodiversity, recreation) using data from 60 parks in Chengdu. Spatial regression reveals vegetation heterogeneity as the strongest positive predictor while management intensity has negative effects. Internal ecological complexity matters more than park size, supporting spatially explicit management.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の都市緑地政策や生物多様性戦略において、公園管理と生態系サービスのトレードオフを考慮する際に示唆を与える。SSBJやTCFDの自然資本開示文脈でも、都市緑地の炭素貯留・冷却効果定量化手法として参考になる。
In the global GX context
This paper provides empirical evidence from a Chinese megacity on urban park multifunctionality. It shows that vegetation heterogeneity, not size, drives ecosystem services and that intensive management can be counterproductive—relevant for global urban planners and climate adaptation strategies.
👥 読者別の含意
🔬研究者:Researchers studying urban ecosystem services can benefit from the stratified sampling and spatial regression methodology, particularly the GWR model uncovering local drivers.
🏢実務担当者:Urban park managers can use the finding that reducing management intensity and increasing vegetation diversity enhances multifunctionality, guiding cost-effective maintenance.
🏛政策担当者:Policymakers in rapidly urbanizing cities can apply the spatially explicit framework to prioritize vegetation heterogeneity in park planning and avoid over-management.
📄 Abstract(原文)
Infrastructure Urban parks are very essential elements of green infrastructure, which provide various ecosystems services such as regulation of microclimates, sequestration of carbon, preservation of biodiversity, and recreation. But the size itself does not dictate the performance of parks. The paper examines the potential of the trio of linked aspects landscape context, management intensity, and vegetation heterogeneity to collectively influence urban park multifunctionality in Chengdu, a fast-urbanizing subtropical mega city. A stratified 3 x 3 x 3 sampling design was applied to categorize the parks by gradients of impervious cover surrounding maintenance intensity surrounding the park and vegetation diversity giving the 60 representative parks under 19 functional combinations. Multifunctionality has been measured based on the cooling intensity, storage of carbon, biodiversity, and recreational value. Spatial autocorrelations were studied with the help of the Morans I, and Ordinary Least Squares (OLS) and geographically weighted regression (GWR) were used to measure both global and local drivers. Findings show that vegetation heterogeneity is the most significant positive predictor of multifunctionality (β = 4.04, p < 0.001), which has a significant positive impact on biodiversity and cooling capacity. However, the management intensity exhibits a strong negative correlation (β = −0.058, p < 0.001), which indicates the theory of making ecological trade-offs in the intensive maintenance regimes. Independent effects of park area and surrounding impervious cover are weaker than internal composition when internal composition is taken into consideration. Multifunctionality is highly clustered in space, which is mainly identified using spatial analysis, and GWR is seen to enhance the model performance (Adjusted R2 = 0.78) considerably by capturing the local variation. The results have shown that, urban park multifunctionality develops as a result of interaction between inner ecological complexity and urban pressures instead of park size. Improving the vegetation diversity and moderated and spatially differentiated management approaches especially in densely populated areas can significantly aid in delivering ecosystem services. The suggested stratified and spatially explicit model offers a generalizable model towards the optimization of urban park planning in fast developing urban areas. References Ahmed, I., & Asif, M. (2026a). The Role of HR in Managing Quiet Quitting and Employee Disengagement in Gen Z Employees of Telecom Sector. Policy Journal of Social Science Review, 4(6), 118-151. https://doi.org/10.5281/zenodo.20581688 Ahmed, S., & Asif, M. (2026b). The impact of hybrid working on employee well-being with the moderating role of organizational performance: A case study of IT sector in Pakistan. Qualitative Research Journal for Social Studies, 3(2), 1006-1030. https://doi.org/10.63878/qrjs1173 Ashinze, U. K., Edeigba, B. A., Umoh, A. A., Biu, P. W., & Daraojimba, A. I. (2024). Urban green infrastructure and its role in sustainable cities: A comprehensive review. World Journal of Advanced Research and Reviews, 21(2), 928–936. Asif, M., Abid, M., & Riaz, A. (2026). Psychological drivers of investment decision making: A multi‑bias analysis of an emerging market’s retail investors. Contemporary Journal of Social Science Review, 4(2), 677–688. https://doi.org/10.63878/cjssr.v4i2.2608 Aznarez, C., Svenning, J.-C., Taveira, G., Baró, F., & Pascual, U. (2022). Wildness and habitat quality drive spatial patterns of urban biodiversity. Landscape and Urban Planning, 228, 104570. https://doi.org/10.1016/j.landurbplan.2022.104570 Borysiak, J., & Stępniewska, M. (2022). Perception of the vegetation cover pattern promoting biodiversity in urban parks by future greenery managers. Land, 11(3), 341. https://doi.org/10.3390/land11030341 Chen, L., Peng, P., Zhu, E., Wu, H., & Feng, D. (2025). Fairness of urban park layout from the perspective of multidimensional supply and demand relationship. Urban Forestry & Urban Greening, 102, 129016. https://doi.org/10.1016/j.ufug.2025.129016 Dizdaroglu, D. (2022). Developing design criteria for sustainable urban parks. Journal of Contemporary Urban Affairs, 6(1), 69–81. https://doi.org/10.25034/ijcua.2022.v6n1-6 Fan, L., Cui, X., & Wang, G. (2024). Impact of urban functional dynamics on surface temperature: A case study of Chengdu. Land, 13(12), 2181. https://doi.org/10.3390/land13122181 Giedych, R., Maksymiuk, G., & Cieszewska, A. (2024). Eco-spatial indices as an effective tool for climate change adaptation in residential neighbourhoods—Comparative study. Land, 13(9), 1492. https://doi.org/10.3390/land13091492 Guo, S., Yang, G., Pei, T., Ma, T., Song, C., Shu, H., Du, Y., & Zhou, C. (2019). Analysis of factors affecting urban park service area in Beijing: Perspectives from multi-source geographic data. Landscape and Urban Planning, 181, 103–117. https://doi.org/10.1016/j.landurbplan.2018.10.001 Haase, D., Larondelle, N., Andersson, E., Artmann, M., Borgström, S., Breuste, J., Gomez-Baggethun, E., Gren, Å., Hamstead, Z., & Hansen, R. (2014). A quantitative review of urban ecosystem service assessments: Concepts, models, and implementation. Ambio, 43(4), 413–433. https://doi.org/10.1007/s13280-014-0504-0 Halecki, W., Stachura, T., Fudała, W., Stec, A., & Kuboń, S. (2023). Assessment and planning of green spaces in urban parks: A review. Sustainable Cities and Society, 88, 104280. https://doi.org/10.1016/j.scs.2022.104280 Han, D., Zhang, T., Qin, Y., Tan, Y., & Liu, J. (2023). A comparative review on the mitigation strategies of urban heat island (UHI): A pathway for sustainable urban development. Climate and Development, 15(5), 379–403. https://doi.org/10.1080/17565529.2022.2081437 Iqbal, U. (2024). AI-enhanced network optimization for electric vehicle charging infrastructure expansion in the United States using graph theory and demand analytics. Journal of Engineering and Computational Intelligence Review, 2(2), 112–129. Iqbal, U. (2025a). AI-driven predictive maintenance for US smart manufacturing: Deep learning models for equipment failure prediction and operational resilience. Journal of Engineering and Computational Intelligence Review, 3(1), 114–138. Iqbal, U. (2025b). AI-powered supplier risk intelligence: Predicting financial and geopolitical supply chain disruptions in US critical industries. Journal of Engineering and Computational Intelligence Review, 3(2), 173–193. Iqbal, U., Bekmez, S., & Qurashi, F. A. (2026). Operational risk management through machine learning and business intelligence in US businesses. Spanish Journal of Innovation and Integrity, 54, 239–253. Iqbal, U., & Bhutto, Y. (2026). Digital transformation through artificial intelligence and advance business analytic in American operational management. Journal of Theoretical and Applied Econometrics, 3(1), 37–50. Jimenez, M. P., Elliott, E. G., DeVille, N. V., Laden, F., Hart, J. E., Weuve, J., Grodstein, F., & James, P. (2022). Residential green space and cognitive function in a large cohort of middle-aged women. JAMA Network Open, 5(4), e229306. https://doi.org/10.1001/jamanetworkopen.2022.9306 Khan, R. D. A., Ping, H., & Asif, M. (2026). The impact of green human resource management on employee green performance through green commitment and transformational leadership. Center for Management Science Research, 4(5), 635–677. https://doi.org/10.5281/zenodo.20510765 Kodym, A., Lapin, K., & Sanyal, D. (2025). Ecological connectivity in urban and semi-urban forests. In Ecological connectivity of forest ecosystems (pp. 365–381). Springer. https://doi.org/10.1007/978-3-031-89412-0_16 Li, X., Li, X., Zhang, M., Luo, Q., Li, Y., & Dong, L. (2024). Urban park attributes as predictors for the diversity and composition of spontaneous plants: A case in Beijing, China. Urban Forestry & Urban Greening, 91, 128185. https://doi.org/10.1016/j.ufug.2023.128185 Lin, B. B., Gaston, K. J., Fuller, R. A., Wu, D., Bush, R., & Shanahan, D. F. (2017). How green is your garden? Urban form and socio-demographic factors influence yard vegetation, visitation, and ecosystem service benefits. Landscape and Urban Planning, 157, 239–246. https://doi.org/10.1016/j.landurbplan.2016.07.007 Ma, Q., Zhang, J., & Li, Y. (2024). Advanced integration of urban street greenery and pedestrian flow: A multidimensional analysis in Chengdu's central urban district. ISPRS International Journal of Geo-Information, 13(7), 254. https://doi.org/10.3390/ijgi13070254 Meng, F., Ren, Z., Zhang, P., Wang, C., Hong, S., Geng, R., Hong, W., Wang, X., Huang, B., & Zhang, B. (2025). Estimation of the relationship between urban landscape pattern and crop yield by remote sensing data and field measurement. Remote Sensing, 17(22), 3667. https://doi.org/10.3390/rs17223667 Mexia, T., Vieira, J., Príncipe, A., Anjos, A., Silva, P., Lopes, N., Freitas, C., Santos-Reis, M., Correia, O., & Branquinho, C. (2018). Ecosystem services: Urban parks under a magnifying glass. Environmental Research, 160, 469–478. https://doi.org/10.1016/j.envres.2017.10.023 Miao, X., Pan, Y., Chen, H., Zhang, M.-J., Hu, W., Li, Y., Wu, R., Wang, P., Fang, S., & Niu, K. (2023). Understanding spontaneous biodiversity in informal urban green spaces: A local-landscape filtering framework with a test on wall plants. Urban Forestry & Urban Greening, 86, 127996. https://doi.org/10.1016/j.ufug.2023.127996 Priya, U. K., & Senthil, R. (2024). Framework for enhancing urban living through sustainable plant selection in residential green spaces. Urban Science, 8(4), 235. https://doi.org/10.3390/urbansci8040235 Szulczewska, B., Giedych, R., & Maksymiuk, G. (2017). Urban park as a subject of research in the 21st century in Poland, on the basis of CEON database. Architektura Krajobrazu, 4, 16–31. Tams, L., Paton, E. N., & Kluge, B. (2023). Impact of shading on evapotranspiration and water
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